IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v230y2023ics0951832022005531.html
   My bibliography  Save this article

Data-driven Bayesian network for risk analysis of global maritime accidents

Author

Listed:
  • Li, Huanhuan
  • Ren, Xujie
  • Yang, Zaili

Abstract

Maritime risk research often suffers from insufficient data for accurate prediction and analysis. This paper aims to conduct a new risk analysis by incorporating the latest maritime accident data into a Bayesian network (BN) model to analyze the key risk influential factors (RIFs) in the maritime sector. It makes important contributions in terms of a novel maritime accident database, new RIFs, findings, and implications. More specifically, the latest maritime accident data from 2017 to 2021 is collected from both the Global Integrated Shipping Information System (GISIS) and Lloyd’s Register Fairplay (LRF) databases. Based on the new dataset, 23 RIFs are identified, involving both dynamic and static risk factors. With these developments, new findings and implications are revealed beyond the state-of-the-art of maritime risk analysis. For instance, the research results show ship type, ship operation, voyage segment, deadweight, length, and power are among the most influencing factors. The new BN-based risk model offers reliable and accurate risk prediction results, evident by its prediction performance and scenario analysis. It provides valuable insights into the development of rational accident prevention measures that could well fit the increasing demands of maritime safety in today’s complex shipping environment.

Suggested Citation

  • Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005531
    DOI: 10.1016/j.ress.2022.108938
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022005531
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108938?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    2. Fu, Shanshan & Zhang, Di & Montewka, Jakub & Yan, Xinping & Zio, Enrico, 2016. "Towards a probabilistic model for predicting ship besetting in ice in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 124-136.
    3. Pristrom, Sascha & Yang, Zaili & Wang, Jin & Yan, Xinping, 2016. "A novel flexible model for piracy and robbery assessment of merchant ship operations," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 196-211.
    4. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    5. Yang, Zaili & Yang, Zhisen & Smith, John & Robert, Bostock Adam Peter, 2021. "Risk analysis of bicycle accidents: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Wang, Shuaian & Yan, Ran & Qu, Xiaobo, 2019. "Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 129-157.
    7. Meizhi Jiang & Jing Lu & Zaili Yang & Jing Li, 2020. "Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(6), pages 815-832, August.
    8. Meizhi Jiang & Jing Lu, 2020. "Maritime accident risk estimation for sea lanes based on a dynamic Bayesian network," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 649-664, July.
    9. Ozcan Arslan & Osman Turan, 2009. "Analytical investigation of marine casualties at the Strait of Istanbul with SWOT--AHP method," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(2), pages 131-145, April.
    10. Z. L. Yang & J. Wang & S. Bonsall & Q. G. Fang, 2009. "Use of Fuzzy Evidential Reasoning in Maritime Security Assessment," Risk Analysis, John Wiley & Sons, vol. 29(1), pages 95-120, January.
    11. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    12. Nguyen, Son & Shu-Ling Chen, Peggy & Du, Yuquan, 2022. "Risk assessment of maritime container shipping blockchain-integrated systems: An analysis of multi-event scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    13. Xiaoyuan Zhao & Haiwen Yuan & Qing Yu, 2021. "Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    14. Jinfen Zhang & Ângelo P Teixeira & C. Guedes Soares & Xinping Yan & Kezhong Liu, 2016. "Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1171-1187, June.
    15. Shiqi Fan & Zaili Yang & Eduardo Blanco-Davis & Jinfen Zhang & Xinping Yan, 2020. "Analysis of maritime transport accidents using Bayesian networks," Journal of Risk and Reliability, , vol. 234(3), pages 439-454, June.
    16. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    17. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    18. Notteboom Theo E., 2004. "Container Shipping And Ports: An Overview," Review of Network Economics, De Gruyter, vol. 3(2), pages 1-21, June.
    19. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    20. Zhang, D. & Yan, X.P. & Yang, Z.L. & Wall, A. & Wang, J., 2013. "Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 93-105.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Domeh, Vindex & Obeng, Francis & Khan, Faisal & Bose, Neil & Sanli, Elizabeth, 2023. "An operational risk awareness tool for small fishing vessels operating in harsh environment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Barr, John & Rabitz, Herschel, 2023. "Kernel-based global sensitivity analysis obtained from a single data set," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Wang, Shuang & Jia, Haiying & Lu, Jing & Yang, Dong, 2023. "Crude oil transportation route choices: A connectivity reliability-based approach," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Zhang, Hengqi & Geng, Hua & Zeng, Huarong & Jiang, Li, 2023. "Dynamic risk evaluation and control of electrical personal accidents," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Li, Huanhuan & Jiao, Hang & Yang, Zaili, 2023. "AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    9. Lilli, Giordano & Sanavia, Matteo & Oboe, Roberto & Vianello, Chiara & Manzolaro, Mattia & De Ruvo, Pasquale Luca & Andrighetto, Alberto, 2024. "A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. Xiaoyuan Zhao & Haiwen Yuan & Qing Yu, 2021. "Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    3. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Kaptan, Mehmet & Uğurlu, Özkan & Wang, Jin, 2021. "The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Shiqi Fan & Zaili Yang & Eduardo Blanco-Davis & Jinfen Zhang & Xinping Yan, 2020. "Analysis of maritime transport accidents using Bayesian networks," Journal of Risk and Reliability, , vol. 234(3), pages 439-454, June.
    8. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
    10. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    11. Yu, Qing & Liu, Kezhong & Chang, Chia-Hsun & Yang, Zaili, 2020. "Realising advanced risk assessment of vessel traffic flows near offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. Liang, Xinrui & Fan, Shiqi & Lucy, John & Yang, Zaili, 2022. "Risk analysis of cargo theft from freight supply chains using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    13. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    14. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    15. Li, Baode & Lu, Jing & Li, Jing & Zhu, Xuebin & Huang, Chuan & Su, Wan, 2022. "Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    16. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    18. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Liangxia Zhong & Jiaxin Wu & Yiqing Wen & Bingjie Yang & Manel Grifoll & Yunping Hu & Pengjun Zheng, 2023. "Analysis of Factors Affecting the Effectiveness of Oil Spill Clean-Up: A Bayesian Network Approach," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    20. Xu, Sheng & Kim, Ekaterina & Haugen, Stein & Zhang, Mingyang, 2022. "A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.